Flash Attention addresses the memory bottleneck in transformer models by eliminating the need to write the large attention matrix to memory. This technique allows for the computation of exact attention without the quadratic memory blow-up typically associated with longer contexts. It optimizes GPU memory usage while maintaining the original mathematical output of the self-attention mechanism.